title: Large-scale learning of combinatorial transcriptional dynamics from gene expression.
abstract: BACKGROUND
  Knowledge of the activation patterns of transcription factors (TFs) is fundamental to elucidate the dynamics of gene regulation in response to environmental conditions. Direct experimental measurement of TFs' activities is, however, challenging, resulting in a need to develop statistical tools to infer TF activities from mRNA expression levels of target genes. Current models, however, neglect important features of transcriptional regulation; in particular, the combinatorial nature of regulation, which is fundamental for signal integration, is not accounted for.
  
  
  RESULTS
  We present a novel method to infer combinatorial regulation of gene expression by multiple transcription factors in large-scale transcriptional regulatory networks. The method implements a factorial hidden Markov model with a non-linear likelihood to represent the interactions between the hidden transcription factors. We explore our model's performance on artificial datasets and demonstrate the applicability of our method on genome-wide scale for three expression datasets. The results obtained using our model are biologically coherent and provide a tool to explore the concealed nature of combinatorial transcriptional regulation.
  
  
  BACKGROUND
  http://homepages.inf.ed.ac.uk/gsanguin/software.html.
authors.affiliation: School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh, UK.
authors.forename: H M Shahzad
authors.initials: HM
authors.lastname: Asif
authors.forename: Guido
authors.initials: G
authors.lastname: Sanguinetti
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D016013:Likelihood Functions; D008390:Markov Chains; D008957:Models, Genetic; D012984:Software; D014157:Transcription Factors / Q000235:genetics* / Q000378:metabolism
chemical_list: D014157:Transcription Factors